Mathematical foundation, basic statistical analysis and data visualization: Matrix Algebra, Probability and random processes, inferential statistics, optimization for data science and data visualization;
Elements of machine learning: Supervised learning (methods: linear and logistic regression, K-NN, LDA, Tree based methods, SVMs, ANNs. Concepts: Bias-Variance tradeoff, regularization, dimensionality reduction), unsupervised learning (clustering and ARM); (iii) Advanced topics: Reinforcement Learning, Deep Learning, Topics in Big Data and Learning Theory.
Balaraman Ravindran, IIT Madras
Mitesh Khapra, IIT Madras
Raghunathan Rengaswamy, IIT Madras
Harish Guruprasad, IIT Madras
Nandan Sudarsanam, IIT Madras
Sahely Bhadra - IIT Palakkad
Sourangshu Bhattachrya, IIT Kharagpur
Anirban Dasgupta, IIT Gandhinagar
Piyush Rai, IIT Kanpur
The course requires an understanding of algebra, calculus, and proficiency in programming.
Nandan Sudarsanam, IIT Madrass
Harish Guruprasad, IIT Madras
Balaraman Ravindran, IIT Madras